A method is described for determining the position of a vehicle equipped with a radar system that includes at least one radar sensor adapted to receive radar signals emitted from at least one radar emitter of the radar system and reflected the radar sensor. The method comprises: acquiring at least one radar scan comprising a plurality of radar detection points, wherein each radar detection point is evaluated from a radar signal received at the radar sensor and representing a location in the vicinity of the vehicle; determining, from a database, a predefined map, wherein the map comprises at least one element representing a static landmark in the vicinity of the vehicle; matching at least a subset of the plurality of radar detection points of the at least one scan and the at least one element of the map; deter-mining the position of the vehicle based on the matching.
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2. The method of claim 1, wherein the method does not comprise using data from a space-based radio-navigation system of the vehicle.
5. The method of claim 1, wherein determining the rigid transformation function comprises a probabilistic model, wherein at least one parameter of the probabilistic model represents an expected variance of a respective one of the plurality of radar detection points, wherein the expected variance is non-constant.
8. The method of claim 7, wherein the motion model is determined based on at least one measurement from at least one motion sensor of the vehicle and/or on the basis of at least some of the plurality of radar detection points.
9. The method of claim 8, wherein the measurement from the at least one motion sensor comprises a velocity or a yaw rate of the vehicle.
A system and method for vehicle motion analysis involves using at least one motion sensor to measure vehicle dynamics, such as velocity or yaw rate, to assess the vehicle's movement. The motion sensor data is processed to determine the vehicle's state, which may include speed, rotational movement, or directional changes. This information is used to monitor vehicle performance, detect anomalies, or enhance navigation systems. The system may integrate multiple sensors to improve accuracy and reliability. The method can be applied in autonomous driving, vehicle safety systems, or fleet management to optimize control and decision-making processes. By analyzing velocity or yaw rate, the system provides insights into the vehicle's handling, stability, and responsiveness, enabling real-time adjustments or predictive maintenance. The approach ensures precise tracking of vehicle dynamics under various driving conditions, improving overall safety and efficiency.
10. The method of claim 1, wherein the position of the vehicle comprises coordinates representing a location and an orientation of the vehicle.
11. The method of claim 1, wherein the subset of the plurality of radar detection points includes radar detection points from a plurality of successive radar scans of the radar system, in particular 1 to 20 scans, preferably 10 scans, wherein a scan rate of the radar system is between 10 to 40 Hz, preferably 20 Hz.
A radar system performs multiple successive scans to detect objects in an environment. The system collects radar detection points from these scans, with the number of scans ranging from 1 to 20, preferably 10. The scan rate of the radar system operates between 10 to 40 Hz, with a preferred rate of 20 Hz. These detection points are used to identify and track objects by analyzing their positions and movements across multiple scans. The system processes the subset of detection points to improve accuracy and reliability in object detection, particularly in dynamic environments where objects may move or change position rapidly. The use of multiple scans helps reduce noise and false detections, ensuring more precise tracking. The radar system's adjustable scan rate allows for optimization based on environmental conditions and object movement speeds, enhancing overall detection performance. This method is particularly useful in applications requiring real-time object tracking, such as autonomous vehicles, surveillance, and industrial automation.
13. The vehicle of claim 12, wherein the control and processing unit does not use data from a space-based radio-navigation system of the vehicle to determine the position of the vehicle.
15. The vehicle of claim 12, wherein determining the rigid transformation function comprises a probabilistic model, wherein at least one parameter of the probabilistic model represents an expected variance of a respective one of the plurality of radar detection points, wherein the expected variance is non-constant.
This invention relates to vehicle positioning systems that use radar sensors to improve localization accuracy. The problem addressed is the inherent uncertainty in radar detection points, which can lead to errors in determining a vehicle's precise position. The solution involves a probabilistic model that accounts for varying uncertainties in radar detection points, allowing for more accurate rigid transformation calculations between radar and vehicle coordinate systems. The system includes a vehicle equipped with radar sensors that detect multiple points in the environment. A processing unit calculates a rigid transformation function to align these radar detection points with a known map or reference frame. The key innovation is the use of a probabilistic model where at least one parameter represents the expected variance of each radar detection point. Unlike traditional methods that assume constant uncertainty, this approach allows the variance to vary, improving accuracy in dynamic or noisy environments. The probabilistic model may incorporate factors such as sensor noise, environmental conditions, or detection reliability to refine the transformation function. This method enhances vehicle localization by reducing positional errors caused by inconsistent radar measurements.
16. The vehicle of claim 15, wherein, for a respective radar detection point, the expected variance of the respective radar detection point comprises a first component and a second component, the first component representing the expected variance with respect to a distance between the location in the vicinity of the vehicle represented by the radar detection point and the at least one radar sensor, the second component representing the expected variance with respect to an angle identifying a direction of the location in the vicinity of the vehicle represented by the radar detection point relative to the at least one radar sensor, and the first component being smaller than the second component.
20. The non-transitory computer-readable storage medium of claim 18, wherein determining the rigid transformation function comprises a probabilistic model, at least one parameter of the probabilistic model representing an expected variance of a respective one of the plurality of radar detection points and the expected variance being non-constant.
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January 7, 2019
November 15, 2022
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